Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations11991358
Missing cells62632869
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 GiB
Average record size in memory224.0 B

Variable types

DateTime1
Categorical4
Text5
Numeric18

Alerts

ARR_DELAY is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
CANCELLATION_CODE is highly overall correlated with CANCELLED and 1 other fieldsHigh correlation
CANCELLED is highly overall correlated with ARR_DELAY and 8 other fieldsHigh correlation
CARRIER_DELAY is highly overall correlated with CANCELLEDHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIMEHigh correlation
DEST_AIRPORT_ID is highly overall correlated with DEST_AIRPORT_SEQ_ID and 1 other fieldsHigh correlation
DEST_AIRPORT_SEQ_ID is highly overall correlated with DEST_AIRPORT_ID and 1 other fieldsHigh correlation
DEST_CITY_MARKET_ID is highly overall correlated with DEST_AIRPORT_ID and 1 other fieldsHigh correlation
LATE_AIRCRAFT_DELAY is highly overall correlated with CANCELLEDHigh correlation
MMYYYY is highly overall correlated with CANCELLATION_CODEHigh correlation
NAS_DELAY is highly overall correlated with CANCELLEDHigh correlation
OP_CARRIER is highly overall correlated with OP_UNIQUE_CARRIERHigh correlation
OP_UNIQUE_CARRIER is highly overall correlated with OP_CARRIERHigh correlation
ORIGIN_AIRPORT_ID is highly overall correlated with ORIGIN_AIRPORT_SEQ_ID and 1 other fieldsHigh correlation
ORIGIN_AIRPORT_SEQ_ID is highly overall correlated with ORIGIN_AIRPORT_ID and 1 other fieldsHigh correlation
ORIGIN_CITY_MARKET_ID is highly overall correlated with ORIGIN_AIRPORT_ID and 1 other fieldsHigh correlation
SECURITY_DELAY is highly overall correlated with CANCELLEDHigh correlation
TAXI_IN is highly overall correlated with CANCELLEDHigh correlation
WEATHER_DELAY is highly overall correlated with CANCELLEDHigh correlation
CANCELLED is highly imbalanced (85.8%)Imbalance
DEP_TIME has 234621 (2.0%) missing valuesMissing
DEP_DELAY has 234846 (2.0%) missing valuesMissing
TAXI_OUT has 239344 (2.0%) missing valuesMissing
TAXI_IN has 244367 (2.0%) missing valuesMissing
ARR_TIME has 244362 (2.0%) missing valuesMissing
ARR_DELAY has 268767 (2.2%) missing valuesMissing
CANCELLATION_CODE has 11750698 (98.0%) missing valuesMissing
CARRIER_DELAY has 9870493 (82.3%) missing valuesMissing
WEATHER_DELAY has 9870493 (82.3%) missing valuesMissing
NAS_DELAY has 9870493 (82.3%) missing valuesMissing
SECURITY_DELAY has 9870493 (82.3%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 9870493 (82.3%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 20.82767831)Skewed
SECURITY_DELAY is highly skewed (γ1 = 112.0395807)Skewed
DEP_DELAY has 592302 (4.9%) zerosZeros
ARR_DELAY has 229257 (1.9%) zerosZeros
CARRIER_DELAY has 986724 (8.2%) zerosZeros
WEATHER_DELAY has 1999251 (16.7%) zerosZeros
NAS_DELAY has 1037152 (8.6%) zerosZeros
SECURITY_DELAY has 2111466 (17.6%) zerosZeros
LATE_AIRCRAFT_DELAY has 1058694 (8.8%) zerosZeros

Reproduction

Analysis started2024-09-20 19:01:24.221822
Analysis finished2024-09-20 19:29:33.565584
Duration28 minutes and 9.34 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct3653
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
Minimum2014-07-01 00:00:00
Maximum2024-06-30 00:00:00
2024-09-20T13:29:33.709234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:29:33.921633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

OP_UNIQUE_CARRIER
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
WN
2464151 
DL
1686476 
AA
1589002 
OO
1346595 
UA
1064221 
Other values (16)
3840913 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters23982716
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd rowVX
3rd rowDL
4th rowWN
5th rowEV

Common Values

ValueCountFrequency (%)
WN 2464151
20.5%
DL 1686476
14.1%
AA 1589002
13.3%
OO 1346595
11.2%
UA 1064221
8.9%
B6 495823
 
4.1%
EV 431775
 
3.6%
MQ 416071
 
3.5%
AS 388825
 
3.2%
YX 360992
 
3.0%
Other values (11) 1747427
14.6%

Length

2024-09-20T13:29:34.089257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 2464151
20.5%
dl 1686476
14.1%
aa 1589002
13.3%
oo 1346595
11.2%
ua 1064221
8.9%
b6 495823
 
4.1%
ev 431775
 
3.6%
mq 416071
 
3.5%
as 388825
 
3.2%
yx 360992
 
3.0%
Other values (11) 1747427
14.6%

Most occurring characters

ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

OP_CARRIER
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
WN
2464151 
DL
1686476 
AA
1589002 
OO
1346595 
UA
1064221 
Other values (16)
3840913 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters23982716
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd rowVX
3rd rowDL
4th rowWN
5th rowEV

Common Values

ValueCountFrequency (%)
WN 2464151
20.5%
DL 1686476
14.1%
AA 1589002
13.3%
OO 1346595
11.2%
UA 1064221
8.9%
B6 495823
 
4.1%
EV 431775
 
3.6%
MQ 416071
 
3.5%
AS 388825
 
3.2%
YX 360992
 
3.0%
Other values (11) 1747427
14.6%

Length

2024-09-20T13:29:34.221973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 2464151
20.5%
dl 1686476
14.1%
aa 1589002
13.3%
oo 1346595
11.2%
ua 1064221
8.9%
b6 495823
 
4.1%
ev 431775
 
3.6%
mq 416071
 
3.5%
as 388825
 
3.2%
yx 360992
 
3.0%
Other values (11) 1747427
14.6%

Most occurring characters

ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23982716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4769579
19.9%
O 2971266
12.4%
N 2795375
11.7%
W 2464151
10.3%
L 1692532
 
7.1%
D 1686476
 
7.0%
U 1147700
 
4.8%
E 716785
 
3.0%
V 643835
 
2.7%
9 523306
 
2.2%
Other values (12) 4571711
19.1%
Distinct9449
Distinct (%)0.1%
Missing63399
Missing (%)0.5%
Memory size91.5 MiB
2024-09-20T13:29:34.683117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9864892
Min length3

Characters and Unicode

Total characters71406598
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowN3GAAA
2nd rowN634VA
3rd rowN974DL
4th rowN924WN
5th rowN691CA
ValueCountFrequency (%)
n493ha 5807
 
< 0.1%
n480ha 5800
 
< 0.1%
n491ha 5694
 
< 0.1%
n484ha 5667
 
< 0.1%
n486ha 5663
 
< 0.1%
n492ha 5652
 
< 0.1%
n487ha 5509
 
< 0.1%
n476ha 5466
 
< 0.1%
n479ha 5452
 
< 0.1%
n483ha 5451
 
< 0.1%
Other values (9439) 11871798
99.5%
2024-09-20T13:29:35.229876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 15285084
21.4%
8 4371608
 
6.1%
3 4264972
 
6.0%
7 4187773
 
5.9%
2 4060034
 
5.7%
9 4026712
 
5.6%
6 3812001
 
5.3%
1 3741400
 
5.2%
5 3737769
 
5.2%
4 3702938
 
5.2%
Other values (24) 20216307
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71406598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 15285084
21.4%
8 4371608
 
6.1%
3 4264972
 
6.0%
7 4187773
 
5.9%
2 4060034
 
5.7%
9 4026712
 
5.6%
6 3812001
 
5.3%
1 3741400
 
5.2%
5 3737769
 
5.2%
4 3702938
 
5.2%
Other values (24) 20216307
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71406598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 15285084
21.4%
8 4371608
 
6.1%
3 4264972
 
6.0%
7 4187773
 
5.9%
2 4060034
 
5.7%
9 4026712
 
5.6%
6 3812001
 
5.3%
1 3741400
 
5.2%
5 3737769
 
5.2%
4 3702938
 
5.2%
Other values (24) 20216307
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71406598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 15285084
21.4%
8 4371608
 
6.1%
3 4264972
 
6.0%
7 4187773
 
5.9%
2 4060034
 
5.7%
9 4026712
 
5.6%
6 3812001
 
5.3%
1 3741400
 
5.2%
5 3737769
 
5.2%
4 3702938
 
5.2%
Other values (24) 20216307
28.3%

ORIGIN_AIRPORT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct387
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12661.256
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:35.388483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1530.5848
Coefficient of variation (CV)0.12088728
Kurtosis-1.3072117
Mean12661.256
Median Absolute Deviation (MAD)1568
Skewness0.082049793
Sum1.5182565 × 1011
Variance2342689.8
MonotonicityNot monotonic
2024-09-20T13:29:35.560024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 677160
 
5.6%
13930 524184
 
4.4%
11298 500590
 
4.2%
11292 472741
 
3.9%
12892 379151
 
3.2%
11057 330627
 
2.8%
14107 314020
 
2.6%
12889 300032
 
2.5%
14771 281090
 
2.3%
14747 274205
 
2.3%
Other values (377) 7937558
66.2%
ValueCountFrequency (%)
10135 6929
 
0.1%
10136 3309
 
< 0.1%
10140 39899
0.3%
10141 1457
 
< 0.1%
10146 1732
 
< 0.1%
10154 1848
 
< 0.1%
10155 2693
 
< 0.1%
10157 3087
 
< 0.1%
10158 5915
 
< 0.1%
10165 224
 
< 0.1%
ValueCountFrequency (%)
16869 1121
 
< 0.1%
16218 3452
 
< 0.1%
16133 1
 
< 0.1%
16101 261
 
< 0.1%
15991 1426
 
< 0.1%
15919 18604
0.2%
15897 500
 
< 0.1%
15841 1428
 
< 0.1%
15624 14409
0.1%
15607 1813
 
< 0.1%

ORIGIN_AIRPORT_SEQ_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct760
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1266129.1
Minimum1013503
Maximum1686902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:35.731565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1013503
5-th percentile1039707
Q11129202
median1288903
Q31402702
95-th percentile1489302
Maximum1686902
Range673399
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation153058.22
Coefficient of variation (CV)0.12088674
Kurtosis-1.3072166
Mean1266129.1
Median Absolute Deviation (MAD)156799
Skewness0.082052193
Sum1.5182608 × 1013
Variance2.3426818 × 1010
MonotonicityNot monotonic
2024-09-20T13:29:35.906142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1129202 472741
 
3.9%
1039707 408332
 
3.4%
1129806 341157
 
2.8%
1105703 330627
 
2.8%
1410702 314020
 
2.6%
1474703 274205
 
2.3%
1226603 268259
 
2.2%
1039705 262589
 
2.2%
1320402 257213
 
2.1%
1143302 250263
 
2.1%
Other values (750) 8811952
73.5%
ValueCountFrequency (%)
1013503 1547
 
< 0.1%
1013504 52
 
< 0.1%
1013505 1461
 
< 0.1%
1013506 3869
 
< 0.1%
1013603 3309
 
< 0.1%
1014003 14212
0.1%
1014004 343
 
< 0.1%
1014005 25344
0.2%
1014103 396
 
< 0.1%
1014104 97
 
< 0.1%
ValueCountFrequency (%)
1686902 479
 
< 0.1%
1686901 642
 
< 0.1%
1621802 1852
 
< 0.1%
1621801 1600
 
< 0.1%
1613305 1
 
< 0.1%
1610102 261
 
< 0.1%
1599102 1426
 
< 0.1%
1591905 3604
 
< 0.1%
1591904 9677
0.1%
1591903 89
 
< 0.1%

ORIGIN_CITY_MARKET_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31730.829
Minimum30070
Maximum36133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:36.073693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130647
median31453
Q332467
95-th percentile34524
Maximum36133
Range6063
Interquartile range (IQR)1820

Descriptive statistics

Standard deviation1306.2894
Coefficient of variation (CV)0.041167832
Kurtosis-0.25314046
Mean31730.829
Median Absolute Deviation (MAD)987
Skewness0.82282807
Sum3.8049573 × 1011
Variance1706392.1
MonotonicityNot monotonic
2024-09-20T13:29:36.239245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 685923
 
5.7%
30977 682009
 
5.7%
30397 677160
 
5.6%
30194 629600
 
5.3%
32575 570201
 
4.8%
30325 472741
 
3.9%
30852 469210
 
3.9%
32457 459369
 
3.8%
31453 373617
 
3.1%
31057 330627
 
2.8%
Other values (351) 6640901
55.4%
ValueCountFrequency (%)
30070 1294
 
< 0.1%
30073 1392
 
< 0.1%
30082 71
 
< 0.1%
30107 1273
 
< 0.1%
30113 1551
 
< 0.1%
30135 6929
 
0.1%
30136 3309
 
< 0.1%
30140 39899
0.3%
30141 1457
 
< 0.1%
30146 1732
 
< 0.1%
ValueCountFrequency (%)
36133 1
 
< 0.1%
36101 261
 
< 0.1%
35991 1426
< 0.1%
35897 500
 
< 0.1%
35841 1428
< 0.1%
35582 771
< 0.1%
35569 416
 
< 0.1%
35550 1813
< 0.1%
35497 106
 
< 0.1%
35454 264
 
< 0.1%

ORIGIN
Text

Distinct387
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:36.698112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35974074
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMIA
2nd rowJFK
3rd rowMCO
4th rowLAX
5th rowATL
ValueCountFrequency (%)
atl 677160
 
5.6%
ord 524184
 
4.4%
dfw 500590
 
4.2%
den 472741
 
3.9%
lax 379151
 
3.2%
clt 330627
 
2.8%
phx 314020
 
2.6%
las 300032
 
2.5%
sfo 281090
 
2.3%
sea 274205
 
2.3%
Other values (377) 7937558
66.2%
2024-09-20T13:29:38.181283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4065594
 
11.3%
L 3392977
 
9.4%
S 3025997
 
8.4%
D 2835688
 
7.9%
T 2015900
 
5.6%
O 1913255
 
5.3%
C 1742869
 
4.8%
M 1605987
 
4.5%
F 1508770
 
4.2%
W 1446960
 
4.0%
Other values (16) 12420077
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4065594
 
11.3%
L 3392977
 
9.4%
S 3025997
 
8.4%
D 2835688
 
7.9%
T 2015900
 
5.6%
O 1913255
 
5.3%
C 1742869
 
4.8%
M 1605987
 
4.5%
F 1508770
 
4.2%
W 1446960
 
4.0%
Other values (16) 12420077
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4065594
 
11.3%
L 3392977
 
9.4%
S 3025997
 
8.4%
D 2835688
 
7.9%
T 2015900
 
5.6%
O 1913255
 
5.3%
C 1742869
 
4.8%
M 1605987
 
4.5%
F 1508770
 
4.2%
W 1446960
 
4.0%
Other values (16) 12420077
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4065594
 
11.3%
L 3392977
 
9.4%
S 3025997
 
8.4%
D 2835688
 
7.9%
T 2015900
 
5.6%
O 1913255
 
5.3%
C 1742869
 
4.8%
M 1605987
 
4.5%
F 1508770
 
4.2%
W 1446960
 
4.0%
Other values (16) 12420077
34.5%
Distinct379
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:38.566262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.081675
Min length8

Characters and Unicode

Total characters156867047
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMiami, FL
2nd rowNew York, NY
3rd rowOrlando, FL
4th rowLos Angeles, CA
5th rowAtlanta, GA
ValueCountFrequency (%)
ca 1356866
 
4.9%
tx 1302483
 
4.7%
fl 1001524
 
3.6%
ga 717105
 
2.6%
il 710063
 
2.5%
chicago 682009
 
2.4%
atlanta 677160
 
2.4%
san 654152
 
2.3%
ny 565378
 
2.0%
co 527970
 
1.9%
Other values (456) 19769310
70.7%
2024-09-20T13:29:39.105819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15972662
 
10.2%
a 12090028
 
7.7%
, 11991358
 
7.6%
o 8774610
 
5.6%
e 8182016
 
5.2%
n 7722499
 
4.9%
t 7686368
 
4.9%
l 6941152
 
4.4%
i 5993030
 
3.8%
r 5573961
 
3.6%
Other values (48) 65939363
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156867047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15972662
 
10.2%
a 12090028
 
7.7%
, 11991358
 
7.6%
o 8774610
 
5.6%
e 8182016
 
5.2%
n 7722499
 
4.9%
t 7686368
 
4.9%
l 6941152
 
4.4%
i 5993030
 
3.8%
r 5573961
 
3.6%
Other values (48) 65939363
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156867047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15972662
 
10.2%
a 12090028
 
7.7%
, 11991358
 
7.6%
o 8774610
 
5.6%
e 8182016
 
5.2%
n 7722499
 
4.9%
t 7686368
 
4.9%
l 6941152
 
4.4%
i 5993030
 
3.8%
r 5573961
 
3.6%
Other values (48) 65939363
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156867047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15972662
 
10.2%
a 12090028
 
7.7%
, 11991358
 
7.6%
o 8774610
 
5.6%
e 8182016
 
5.2%
n 7722499
 
4.9%
t 7686368
 
4.9%
l 6941152
 
4.4%
i 5993030
 
3.8%
r 5573961
 
3.6%
Other values (48) 65939363
42.0%

DEST_AIRPORT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct387
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12660.67
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:39.265425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1530.3726
Coefficient of variation (CV)0.12087612
Kurtosis-1.3058999
Mean12660.67
Median Absolute Deviation (MAD)1568
Skewness0.083446859
Sum1.5181862 × 1011
Variance2342040.3
MonotonicityNot monotonic
2024-09-20T13:29:39.437930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 677146
 
5.6%
13930 523625
 
4.4%
11298 501097
 
4.2%
11292 473432
 
3.9%
12892 379305
 
3.2%
11057 331170
 
2.8%
14107 314299
 
2.6%
12889 299584
 
2.5%
14771 280522
 
2.3%
14747 273070
 
2.3%
Other values (377) 7938108
66.2%
ValueCountFrequency (%)
10135 7024
 
0.1%
10136 3260
 
< 0.1%
10140 39909
0.3%
10141 1491
 
< 0.1%
10146 1776
 
< 0.1%
10154 1813
 
< 0.1%
10155 2733
 
< 0.1%
10157 3042
 
< 0.1%
10158 5975
 
< 0.1%
10165 197
 
< 0.1%
ValueCountFrequency (%)
16869 1200
 
< 0.1%
16218 3391
 
< 0.1%
16133 2
 
< 0.1%
16101 262
 
< 0.1%
15991 1481
 
< 0.1%
15919 18630
0.2%
15897 536
 
< 0.1%
15841 1465
 
< 0.1%
15624 14367
0.1%
15607 1847
 
< 0.1%

DEST_AIRPORT_SEQ_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct758
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1266070.5
Minimum1013503
Maximum1686902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:39.603519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1013503
5-th percentile1039707
Q11129202
median1288903
Q31402702
95-th percentile1489302
Maximum1686902
Range673399
Interquartile range (IQR)273500

Descriptive statistics

Standard deviation153037
Coefficient of variation (CV)0.12087558
Kurtosis-1.3059048
Mean1266070.5
Median Absolute Deviation (MAD)156799
Skewness0.083449257
Sum1.5181904 × 1013
Variance2.3420323 × 1010
MonotonicityNot monotonic
2024-09-20T13:29:39.779019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1129202 473432
 
3.9%
1039707 407930
 
3.4%
1129806 341641
 
2.8%
1105703 331170
 
2.8%
1410702 314299
 
2.6%
1474703 273070
 
2.3%
1226603 268983
 
2.2%
1039705 262879
 
2.2%
1320402 257836
 
2.2%
1143302 251283
 
2.1%
Other values (748) 8808835
73.5%
ValueCountFrequency (%)
1013503 1546
 
< 0.1%
1013504 57
 
< 0.1%
1013505 1499
 
< 0.1%
1013506 3922
 
< 0.1%
1013603 3260
 
< 0.1%
1014003 14438
0.1%
1014004 318
 
< 0.1%
1014005 25153
0.2%
1014103 432
 
< 0.1%
1014104 113
 
< 0.1%
ValueCountFrequency (%)
1686902 554
 
< 0.1%
1686901 646
 
< 0.1%
1621802 1809
 
< 0.1%
1621801 1582
 
< 0.1%
1613305 2
 
< 0.1%
1610102 262
 
< 0.1%
1599102 1481
 
< 0.1%
1591905 3504
 
< 0.1%
1591904 9715
0.1%
1591903 75
 
< 0.1%

DEST_CITY_MARKET_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31730.473
Minimum30070
Maximum36133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:39.945610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30070
5-th percentile30194
Q130647
median31453
Q332467
95-th percentile34524
Maximum36133
Range6063
Interquartile range (IQR)1820

Descriptive statistics

Standard deviation1305.9278
Coefficient of variation (CV)0.041156897
Kurtosis-0.24865616
Mean31730.473
Median Absolute Deviation (MAD)987
Skewness0.82394087
Sum3.8049147 × 1011
Variance1705447.5
MonotonicityNot monotonic
2024-09-20T13:29:40.111164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31703 686423
 
5.7%
30977 680709
 
5.7%
30397 677146
 
5.6%
30194 630025
 
5.3%
32575 570790
 
4.8%
30325 473432
 
3.9%
30852 469977
 
3.9%
32457 459179
 
3.8%
31453 374128
 
3.1%
31057 331170
 
2.8%
Other values (351) 6638379
55.4%
ValueCountFrequency (%)
30070 1309
 
< 0.1%
30073 1396
 
< 0.1%
30082 71
 
< 0.1%
30107 1251
 
< 0.1%
30113 1553
 
< 0.1%
30135 7024
 
0.1%
30136 3260
 
< 0.1%
30140 39909
0.3%
30141 1491
 
< 0.1%
30146 1776
 
< 0.1%
ValueCountFrequency (%)
36133 2
 
< 0.1%
36101 262
 
< 0.1%
35991 1481
< 0.1%
35897 536
 
< 0.1%
35841 1465
< 0.1%
35582 732
 
< 0.1%
35569 395
 
< 0.1%
35550 1847
< 0.1%
35497 108
 
< 0.1%
35454 261
 
< 0.1%

DEST
Text

Distinct387
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:40.540981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35974074
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHX
2nd rowSFO
3rd rowLGA
4th rowSTL
5th rowXNA
ValueCountFrequency (%)
atl 677146
 
5.6%
ord 523625
 
4.4%
dfw 501097
 
4.2%
den 473432
 
3.9%
lax 379305
 
3.2%
clt 331170
 
2.8%
phx 314299
 
2.6%
las 299584
 
2.5%
sfo 280522
 
2.3%
sea 273070
 
2.3%
Other values (377) 7938108
66.2%
2024-09-20T13:29:41.087519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4064902
 
11.3%
L 3394790
 
9.4%
S 3023811
 
8.4%
D 2836627
 
7.9%
T 2018906
 
5.6%
O 1911137
 
5.3%
C 1741782
 
4.8%
M 1606190
 
4.5%
F 1510391
 
4.2%
W 1447098
 
4.0%
Other values (16) 12418440
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4064902
 
11.3%
L 3394790
 
9.4%
S 3023811
 
8.4%
D 2836627
 
7.9%
T 2018906
 
5.6%
O 1911137
 
5.3%
C 1741782
 
4.8%
M 1606190
 
4.5%
F 1510391
 
4.2%
W 1447098
 
4.0%
Other values (16) 12418440
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4064902
 
11.3%
L 3394790
 
9.4%
S 3023811
 
8.4%
D 2836627
 
7.9%
T 2018906
 
5.6%
O 1911137
 
5.3%
C 1741782
 
4.8%
M 1606190
 
4.5%
F 1510391
 
4.2%
W 1447098
 
4.0%
Other values (16) 12418440
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4064902
 
11.3%
L 3394790
 
9.4%
S 3023811
 
8.4%
D 2836627
 
7.9%
T 2018906
 
5.6%
O 1911137
 
5.3%
C 1741782
 
4.8%
M 1606190
 
4.5%
F 1510391
 
4.2%
W 1447098
 
4.0%
Other values (16) 12418440
34.5%
Distinct379
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:41.459525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.080979
Min length8

Characters and Unicode

Total characters156858707
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhoenix, AZ
2nd rowSan Francisco, CA
3rd rowNew York, NY
4th rowSt. Louis, MO
5th rowFayetteville, AR
ValueCountFrequency (%)
ca 1357460
 
4.9%
tx 1303577
 
4.7%
fl 1002829
 
3.6%
ga 716987
 
2.6%
il 708552
 
2.5%
chicago 680709
 
2.4%
atlanta 677146
 
2.4%
san 653417
 
2.3%
ny 565989
 
2.0%
co 528129
 
1.9%
Other values (456) 19769892
70.7%
2024-09-20T13:29:41.983124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15973329
 
10.2%
a 12084815
 
7.7%
, 11991358
 
7.6%
o 8780644
 
5.6%
e 8182712
 
5.2%
n 7723542
 
4.9%
t 7690934
 
4.9%
l 6939993
 
4.4%
i 5986361
 
3.8%
r 5577240
 
3.6%
Other values (48) 65927779
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156858707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15973329
 
10.2%
a 12084815
 
7.7%
, 11991358
 
7.6%
o 8780644
 
5.6%
e 8182712
 
5.2%
n 7723542
 
4.9%
t 7690934
 
4.9%
l 6939993
 
4.4%
i 5986361
 
3.8%
r 5577240
 
3.6%
Other values (48) 65927779
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156858707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15973329
 
10.2%
a 12084815
 
7.7%
, 11991358
 
7.6%
o 8780644
 
5.6%
e 8182712
 
5.2%
n 7723542
 
4.9%
t 7690934
 
4.9%
l 6939993
 
4.4%
i 5986361
 
3.8%
r 5577240
 
3.6%
Other values (48) 65927779
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156858707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15973329
 
10.2%
a 12084815
 
7.7%
, 11991358
 
7.6%
o 8780644
 
5.6%
e 8182712
 
5.2%
n 7723542
 
4.9%
t 7690934
 
4.9%
l 6939993
 
4.4%
i 5986361
 
3.8%
r 5577240
 
3.6%
Other values (48) 65927779
42.0%

DEP_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1440
Distinct (%)< 0.1%
Missing234621
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1330.4342
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:42.137744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile604
Q1917
median1324
Q31738
95-th percentile2131
Maximum2400
Range2399
Interquartile range (IQR)821

Descriptive statistics

Standard deviation497.87618
Coefficient of variation (CV)0.37422082
Kurtosis-0.96831036
Mean1330.4342
Median Absolute Deviation (MAD)411
Skewness0.037810536
Sum1.5641565 × 1010
Variance247880.69
MonotonicityNot monotonic
2024-09-20T13:29:42.305296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 30575
 
0.3%
556 28303
 
0.2%
557 27930
 
0.2%
655 26353
 
0.2%
558 26125
 
0.2%
554 24417
 
0.2%
656 24408
 
0.2%
559 24169
 
0.2%
657 23581
 
0.2%
600 22390
 
0.2%
Other values (1430) 11498486
95.9%
(Missing) 234621
 
2.0%
ValueCountFrequency (%)
1 1432
< 0.1%
2 1139
< 0.1%
3 1092
< 0.1%
4 1026
< 0.1%
5 981
< 0.1%
6 959
< 0.1%
7 990
< 0.1%
8 963
< 0.1%
9 944
< 0.1%
10 884
< 0.1%
ValueCountFrequency (%)
2400 971
< 0.1%
2359 1617
< 0.1%
2358 1593
< 0.1%
2357 1655
< 0.1%
2356 1839
< 0.1%
2355 2004
< 0.1%
2354 2043
< 0.1%
2353 2131
< 0.1%
2352 2092
< 0.1%
2351 2010
< 0.1%

DEP_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1782
Distinct (%)< 0.1%
Missing234846
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean9.5235726
Minimum-234
Maximum3360
Zeros592302
Zeros (%)4.9%
Negative7148564
Negative (%)59.6%
Memory size91.5 MiB
2024-09-20T13:29:42.463871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-234
5-th percentile-10
Q1-5
median-2
Q36
95-th percentile69
Maximum3360
Range3594
Interquartile range (IQR)11

Descriptive statistics

Standard deviation45.714361
Coefficient of variation (CV)4.8001273
Kurtosis242.38101
Mean9.5235726
Median Absolute Deviation (MAD)4
Skewness11.203295
Sum1.11964 × 108
Variance2089.8028
MonotonicityNot monotonic
2024-09-20T13:29:42.630424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 953455
 
8.0%
-4 908498
 
7.6%
-3 883157
 
7.4%
-2 802595
 
6.7%
-6 742406
 
6.2%
-1 703421
 
5.9%
-7 597566
 
5.0%
0 592302
 
4.9%
-8 457155
 
3.8%
-9 336325
 
2.8%
Other values (1772) 4779632
39.9%
ValueCountFrequency (%)
-234 1
< 0.1%
-204 1
< 0.1%
-201 1
< 0.1%
-151 1
< 0.1%
-130 1
< 0.1%
-112 1
< 0.1%
-102 1
< 0.1%
-96 1
< 0.1%
-92 1
< 0.1%
-91 1
< 0.1%
ValueCountFrequency (%)
3360 1
< 0.1%
3343 1
< 0.1%
3095 1
< 0.1%
3051 1
< 0.1%
3011 1
< 0.1%
2994 1
< 0.1%
2895 1
< 0.1%
2871 1
< 0.1%
2816 1
< 0.1%
2765 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct189
Distinct (%)< 0.1%
Missing239344
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean16.570201
Minimum1
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:42.798944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile33
Maximum256
Range255
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1586671
Coefficient of variation (CV)0.55271913
Kurtosis22.843441
Mean16.570201
Median Absolute Deviation (MAD)4
Skewness3.3948698
Sum1.9473324 × 108
Variance83.881183
MonotonicityNot monotonic
2024-09-20T13:29:42.962536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 962702
 
8.0%
11 942237
 
7.9%
13 923454
 
7.7%
10 847067
 
7.1%
14 846288
 
7.1%
15 753946
 
6.3%
9 671912
 
5.6%
16 656378
 
5.5%
17 565482
 
4.7%
18 485361
 
4.0%
Other values (179) 4097187
34.2%
ValueCountFrequency (%)
1 293
 
< 0.1%
2 462
 
< 0.1%
3 2667
 
< 0.1%
4 9224
 
0.1%
5 32899
 
0.3%
6 112978
 
0.9%
7 258397
 
2.2%
8 455479
3.8%
9 671912
5.6%
10 847067
7.1%
ValueCountFrequency (%)
256 1
 
< 0.1%
213 1
 
< 0.1%
201 1
 
< 0.1%
198 1
 
< 0.1%
188 1
 
< 0.1%
186 1
 
< 0.1%
183 4
< 0.1%
182 3
< 0.1%
181 1
 
< 0.1%
180 1
 
< 0.1%

TAXI_IN
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct234
Distinct (%)< 0.1%
Missing244367
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean7.5612734
Minimum1
Maximum1426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:43.111141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile17
Maximum1426
Range1425
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0072554
Coefficient of variation (CV)0.79447669
Kurtosis335.58966
Mean7.5612734
Median Absolute Deviation (MAD)2
Skewness6.2853995
Sum88822210
Variance36.087118
MonotonicityNot monotonic
2024-09-20T13:29:43.275697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1842450
15.4%
5 1774571
14.8%
6 1447096
12.1%
3 1205823
10.1%
7 1130520
9.4%
8 839388
7.0%
9 641651
 
5.4%
10 495265
 
4.1%
11 380283
 
3.2%
2 331134
 
2.8%
Other values (224) 1658810
13.8%
ValueCountFrequency (%)
1 19561
 
0.2%
2 331134
 
2.8%
3 1205823
10.1%
4 1842450
15.4%
5 1774571
14.8%
6 1447096
12.1%
7 1130520
9.4%
8 839388
7.0%
9 641651
 
5.4%
10 495265
 
4.1%
ValueCountFrequency (%)
1426 1
< 0.1%
414 1
< 0.1%
400 1
< 0.1%
399 2
< 0.1%
397 1
< 0.1%
365 1
< 0.1%
349 1
< 0.1%
344 1
< 0.1%
341 1
< 0.1%
315 1
< 0.1%

ARR_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1440
Distinct (%)< 0.1%
Missing244362
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1470.5384
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:43.445247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile656
Q11055
median1508
Q31914
95-th percentile2248
Maximum2400
Range2399
Interquartile range (IQR)859

Descriptive statistics

Standard deviation529.03114
Coefficient of variation (CV)0.35975336
Kurtosis-0.36810708
Mean1470.5384
Median Absolute Deviation (MAD)410
Skewness-0.35553999
Sum1.7274409 × 1010
Variance279873.94
MonotonicityNot monotonic
2024-09-20T13:29:43.608809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1643 13038
 
0.1%
1641 13008
 
0.1%
1646 12926
 
0.1%
1634 12921
 
0.1%
1638 12910
 
0.1%
1628 12902
 
0.1%
1630 12898
 
0.1%
1645 12871
 
0.1%
1635 12867
 
0.1%
1637 12827
 
0.1%
Other values (1430) 11617828
96.9%
(Missing) 244362
 
2.0%
ValueCountFrequency (%)
1 6484
0.1%
2 5641
< 0.1%
3 5638
< 0.1%
4 5410
< 0.1%
5 5401
< 0.1%
6 5196
< 0.1%
7 5250
< 0.1%
8 4977
< 0.1%
9 4897
< 0.1%
10 4881
< 0.1%
ValueCountFrequency (%)
2400 5456
< 0.1%
2359 6030
0.1%
2358 6237
0.1%
2357 6524
0.1%
2356 6561
0.1%
2355 6739
0.1%
2354 6835
0.1%
2353 6953
0.1%
2352 7128
0.1%
2351 7272
0.1%

ARR_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1794
Distinct (%)< 0.1%
Missing268767
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean3.9905049
Minimum-235
Maximum3359
Zeros229257
Zeros (%)1.9%
Negative7497114
Negative (%)62.5%
Memory size91.5 MiB
2024-09-20T13:29:43.766387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-235
5-th percentile-27
Q1-15
median-6
Q37
95-th percentile68
Maximum3359
Range3594
Interquartile range (IQR)22

Descriptive statistics

Standard deviation47.707003
Coefficient of variation (CV)11.955129
Kurtosis204.77502
Mean3.9905049
Median Absolute Deviation (MAD)10
Skewness9.928557
Sum46779057
Variance2275.9581
MonotonicityNot monotonic
2024-09-20T13:29:43.929952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 351374
 
2.9%
-11 349606
 
2.9%
-9 348180
 
2.9%
-12 346214
 
2.9%
-8 341664
 
2.8%
-13 337155
 
2.8%
-7 332418
 
2.8%
-14 325674
 
2.7%
-6 319403
 
2.7%
-15 313329
 
2.6%
Other values (1784) 8357574
69.7%
ValueCountFrequency (%)
-235 1
< 0.1%
-194 1
< 0.1%
-151 1
< 0.1%
-148 1
< 0.1%
-121 1
< 0.1%
-119 1
< 0.1%
-117 1
< 0.1%
-108 1
< 0.1%
-107 1
< 0.1%
-106 1
< 0.1%
ValueCountFrequency (%)
3359 1
< 0.1%
3337 1
< 0.1%
3089 1
< 0.1%
3045 1
< 0.1%
3027 1
< 0.1%
2977 1
< 0.1%
2900 1
< 0.1%
2854 1
< 0.1%
2795 1
< 0.1%
2748 1
< 0.1%

CANCELLED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.5 MiB
0.0
11750698 
1.0
 
240660

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35974074
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11750698
98.0%
1.0 240660
 
2.0%

Length

2024-09-20T13:29:44.084538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T13:29:44.192285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11750698
98.0%
1.0 240660
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 23742056
66.0%
. 11991358
33.3%
1 240660
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23742056
66.0%
. 11991358
33.3%
1 240660
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23742056
66.0%
. 11991358
33.3%
1 240660
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35974074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23742056
66.0%
. 11991358
33.3%
1 240660
 
0.7%

CANCELLATION_CODE
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing11750698
Missing (%)98.0%
Memory size91.5 MiB
B
98334 
A
58590 
D
56053 
C
27683 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters240660
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowA
5th rowC

Common Values

ValueCountFrequency (%)
B 98334
 
0.8%
A 58590
 
0.5%
D 56053
 
0.5%
C 27683
 
0.2%
(Missing) 11750698
98.0%

Length

2024-09-20T13:29:44.305979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-20T13:29:44.413660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 98334
40.9%
a 58590
24.3%
d 56053
23.3%
c 27683
 
11.5%

Most occurring characters

ValueCountFrequency (%)
B 98334
40.9%
A 58590
24.3%
D 56053
23.3%
C 27683
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 240660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 98334
40.9%
A 58590
24.3%
D 56053
23.3%
C 27683
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 240660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 98334
40.9%
A 58590
24.3%
D 56053
23.3%
C 27683
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 240660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 98334
40.9%
A 58590
24.3%
D 56053
23.3%
C 27683
 
11.5%

CARRIER_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1581
Distinct (%)0.1%
Missing9870493
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean22.324728
Minimum0
Maximum3359
Zeros986724
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:44.552323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile97
Maximum3359
Range3359
Interquartile range (IQR)20

Descriptive statistics

Standard deviation66.034238
Coefficient of variation (CV)2.9578966
Kurtosis170.95033
Mean22.324728
Median Absolute Deviation (MAD)2
Skewness10.234107
Sum47347735
Variance4360.5206
MonotonicityNot monotonic
2024-09-20T13:29:44.715883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 986724
 
8.2%
1 38989
 
0.3%
2 38553
 
0.3%
3 37386
 
0.3%
6 36744
 
0.3%
4 36368
 
0.3%
5 34735
 
0.3%
7 34064
 
0.3%
15 33774
 
0.3%
8 32314
 
0.3%
Other values (1571) 811214
 
6.8%
(Missing) 9870493
82.3%
ValueCountFrequency (%)
0 986724
8.2%
1 38989
 
0.3%
2 38553
 
0.3%
3 37386
 
0.3%
4 36368
 
0.3%
5 34735
 
0.3%
6 36744
 
0.3%
7 34064
 
0.3%
8 32314
 
0.3%
9 29920
 
0.2%
ValueCountFrequency (%)
3359 1
< 0.1%
3337 1
< 0.1%
3089 1
< 0.1%
3045 1
< 0.1%
3027 1
< 0.1%
2977 1
< 0.1%
2742 1
< 0.1%
2675 1
< 0.1%
2660 1
< 0.1%
2653 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1107
Distinct (%)0.1%
Missing9870493
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean3.5106841
Minimum0
Maximum2475
Zeros1999251
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:44.886431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum2475
Range2475
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.157121
Coefficient of variation (CV)8.3052533
Kurtosis646.26758
Mean3.5106841
Median Absolute Deviation (MAD)0
Skewness20.827678
Sum7445687
Variance850.13768
MonotonicityNot monotonic
2024-09-20T13:29:45.049987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1999251
 
16.7%
7 2608
 
< 0.1%
15 2600
 
< 0.1%
6 2600
 
< 0.1%
2 2497
 
< 0.1%
16 2485
 
< 0.1%
5 2436
 
< 0.1%
8 2435
 
< 0.1%
3 2435
 
< 0.1%
9 2409
 
< 0.1%
Other values (1097) 99109
 
0.8%
(Missing) 9870493
82.3%
ValueCountFrequency (%)
0 1999251
16.7%
1 2324
 
< 0.1%
2 2497
 
< 0.1%
3 2435
 
< 0.1%
4 2380
 
< 0.1%
5 2436
 
< 0.1%
6 2600
 
< 0.1%
7 2608
 
< 0.1%
8 2435
 
< 0.1%
9 2409
 
< 0.1%
ValueCountFrequency (%)
2475 1
< 0.1%
2363 1
< 0.1%
2098 1
< 0.1%
1747 1
< 0.1%
1728 1
< 0.1%
1581 1
< 0.1%
1561 1
< 0.1%
1552 1
< 0.1%
1529 1
< 0.1%
1525 1
< 0.1%

NAS_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct930
Distinct (%)< 0.1%
Missing9870493
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean13.868871
Minimum0
Maximum1660
Zeros1037152
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:45.203573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318
95-th percentile58
Maximum1660
Range1660
Interquartile range (IQR)18

Descriptive statistics

Standard deviation32.058563
Coefficient of variation (CV)2.311548
Kurtosis211.02129
Mean13.868871
Median Absolute Deviation (MAD)1
Skewness9.707252
Sum29414004
Variance1027.7514
MonotonicityNot monotonic
2024-09-20T13:29:45.366148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1037152
 
8.6%
1 50704
 
0.4%
15 44094
 
0.4%
2 41394
 
0.3%
16 39887
 
0.3%
3 38763
 
0.3%
4 36557
 
0.3%
17 36197
 
0.3%
5 34474
 
0.3%
18 32663
 
0.3%
Other values (920) 728980
 
6.1%
(Missing) 9870493
82.3%
ValueCountFrequency (%)
0 1037152
8.6%
1 50704
 
0.4%
2 41394
 
0.3%
3 38763
 
0.3%
4 36557
 
0.3%
5 34474
 
0.3%
6 32157
 
0.3%
7 30263
 
0.3%
8 28658
 
0.2%
9 27082
 
0.2%
ValueCountFrequency (%)
1660 1
< 0.1%
1642 1
< 0.1%
1516 1
< 0.1%
1508 1
< 0.1%
1436 1
< 0.1%
1427 1
< 0.1%
1425 1
< 0.1%
1419 1
< 0.1%
1407 1
< 0.1%
1404 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct259
Distinct (%)< 0.1%
Missing9870493
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean0.12518289
Minimum0
Maximum1183
Zeros2111466
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:45.730207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1183
Range1183
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6422224
Coefficient of variation (CV)29.095211
Kurtosis22780.459
Mean0.12518289
Median Absolute Deviation (MAD)0
Skewness112.03958
Sum265496
Variance13.265784
MonotonicityNot monotonic
2024-09-20T13:29:45.894767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2111466
 
17.6%
15 420
 
< 0.1%
16 335
 
< 0.1%
18 321
 
< 0.1%
8 316
 
< 0.1%
17 313
 
< 0.1%
10 304
 
< 0.1%
12 274
 
< 0.1%
7 268
 
< 0.1%
11 267
 
< 0.1%
Other values (249) 6581
 
0.1%
(Missing) 9870493
82.3%
ValueCountFrequency (%)
0 2111466
17.6%
1 200
 
< 0.1%
2 233
 
< 0.1%
3 246
 
< 0.1%
4 253
 
< 0.1%
5 239
 
< 0.1%
6 267
 
< 0.1%
7 268
 
< 0.1%
8 316
 
< 0.1%
9 265
 
< 0.1%
ValueCountFrequency (%)
1183 1
< 0.1%
1091 1
< 0.1%
987 1
< 0.1%
983 1
< 0.1%
816 1
< 0.1%
789 1
< 0.1%
738 1
< 0.1%
691 1
< 0.1%
656 1
< 0.1%
653 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1256
Distinct (%)0.1%
Missing9870493
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean24.981462
Minimum0
Maximum2690
Zeros1058694
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:46.055374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q330
95-th percentile114
Maximum2690
Range2690
Interquartile range (IQR)30

Descriptive statistics

Standard deviation52.300303
Coefficient of variation (CV)2.0935646
Kurtosis106.21905
Mean24.981462
Median Absolute Deviation (MAD)1
Skewness6.8471653
Sum52982308
Variance2735.3217
MonotonicityNot monotonic
2024-09-20T13:29:46.220934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1058694
 
8.8%
15 26673
 
0.2%
16 25093
 
0.2%
17 23807
 
0.2%
18 22821
 
0.2%
19 21530
 
0.2%
20 20662
 
0.2%
21 19647
 
0.2%
14 19419
 
0.2%
13 19055
 
0.2%
Other values (1246) 863464
 
7.2%
(Missing) 9870493
82.3%
ValueCountFrequency (%)
0 1058694
8.8%
1 16375
 
0.1%
2 16155
 
0.1%
3 15670
 
0.1%
4 15372
 
0.1%
5 15471
 
0.1%
6 16596
 
0.1%
7 16394
 
0.1%
8 17085
 
0.1%
9 17414
 
0.1%
ValueCountFrequency (%)
2690 1
< 0.1%
2258 1
< 0.1%
2228 1
< 0.1%
2098 1
< 0.1%
2096 1
< 0.1%
2093 1
< 0.1%
2088 1
< 0.1%
2010 1
< 0.1%
2006 1
< 0.1%
1926 1
< 0.1%

MMYYYY
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201906.46
Minimum201407
Maximum202406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.5 MiB
2024-09-20T13:29:46.378510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum201407
5-th percentile201412
Q1201612
median201906
Q3202201
95-th percentile202401
Maximum202406
Range999
Interquartile range (IQR)589

Descriptive statistics

Standard deviation291.12048
Coefficient of variation (CV)0.0014418582
Kurtosis-1.1631919
Mean201906.46
Median Absolute Deviation (MAD)294
Skewness0.00042383716
Sum2.4211327 × 1012
Variance84751.136
MonotonicityNot monotonic
2024-09-20T13:29:46.548059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201410 100000
 
0.8%
201609 100000
 
0.8%
202105 100000
 
0.8%
202111 100000
 
0.8%
202311 100000
 
0.8%
201711 99999
 
0.8%
201611 99999
 
0.8%
201705 99999
 
0.8%
202002 99999
 
0.8%
202303 99999
 
0.8%
Other values (110) 10991363
91.7%
ValueCountFrequency (%)
201407 99989
0.8%
201408 99996
0.8%
201409 99990
0.8%
201410 100000
0.8%
201411 99996
0.8%
201412 99995
0.8%
201501 99861
0.8%
201502 99844
0.8%
201503 99953
0.8%
201504 99996
0.8%
ValueCountFrequency (%)
202406 99996
0.8%
202405 99998
0.8%
202404 99999
0.8%
202403 99996
0.8%
202402 99996
0.8%
202401 99947
0.8%
202312 99997
0.8%
202311 100000
0.8%
202310 99999
0.8%
202309 99997
0.8%

Interactions

2024-09-20T13:25:01.131545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:09.947218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:10.651047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:42.594146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:14.747452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:46.781270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:18.489477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:50.426075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:22.354695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:53.264044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:24.948312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:56.079489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:27.926326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:57.981949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:08.962688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:20.052137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:31.104844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:42.224104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:09.647771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:11.644680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:12.247778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:44.268635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:16.489203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:48.523610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:20.361472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:52.210304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:23.977358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:54.935569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:26.646774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:57.877686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:29.625782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:58.481613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:09.466373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:20.556784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:31.807960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:42.692853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:18.077225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:13.266488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:13.863490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:45.862374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:18.239596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:50.270939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:22.100789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:53.983562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:25.689776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:56.665941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:28.531730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:59.651938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:31.343192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:58.940392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:09.975978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:21.023540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:32.277703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:43.150629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:26.858716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:14.983367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:15.634755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:47.641616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:19.828349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:51.875615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:23.747413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:55.936340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:27.459013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:58.383352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:30.238135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:01.411232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:33.015717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:59.441021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:10.458715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:21.512238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:32.748450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:43.637327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:35.579427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:16.742662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:17.416954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:49.420892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:21.493891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:53.503295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:25.425897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:57.689654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:29.112622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:00.282239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:31.902683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:03.147588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:34.743066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:59.915783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:10.920489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:21.972004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:33.210212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:44.104082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:44.064731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:18.482977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:19.112452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:51.085438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:23.400792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:55.138920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:26.960824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:59.470889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:30.816067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:02.134319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:33.677967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:04.919849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:36.477459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:00.413420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:11.423110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:22.481640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:33.678959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:44.611721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:25:52.697651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:20.187452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:20.876742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:52.843738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:25.102242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:56.880264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:28.760980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:01.088560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:32.492554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:03.809807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:35.444241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:06.724990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:38.081167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:00.875350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:11.876926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:22.948395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:34.144716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:45.069500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:01.355500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:21.936773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:22.656975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:54.621980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:26.869516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:58.681447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:30.583106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:02.795966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:33.996563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:05.532234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:36.994099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:08.486312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:39.797549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:01.370990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:12.375562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:23.444062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:34.618446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:45.566137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:09.945528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:23.706328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:24.416527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:56.407175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:28.618843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:00.448721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:32.440141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:04.512407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:35.638172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:07.120983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:38.643690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:10.178754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:41.545906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:01.830791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:12.834438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:23.916802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:35.072233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:46.054857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:18.613356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:14:25.419726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:26.116981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:58.096686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:30.364172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:02.125238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:34.137633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:06.168947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:37.286765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:08.813458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:40.257767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:11.838348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:43.152604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:02.303531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:13.302188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:24.415471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:35.558932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:46.541555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:27.119604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:55.858639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:27.876275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:59.908844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:32.062598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:03.866581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:35.870009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:07.871393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:38.867539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:10.502942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:41.827603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:13.533814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:44.764296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:02.763300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:13.772897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:24.885213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:36.007729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:47.031253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:35.683705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:57.658824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:29.643583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:01.645200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:33.824917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:05.634822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:37.630292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:09.581850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:40.529063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:12.196412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:43.495139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:15.201323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:46.370006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:03.230017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:14.277548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:25.354926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:36.493432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:47.559837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:38.843256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:58.153496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:30.131277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:02.133896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:34.353504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:06.123546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:38.137940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:10.104455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:41.009808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:12.725998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:43.961894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:15.725920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:46.860693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:03.651889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:14.774221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:25.804756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:36.949212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:48.044536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:41.865172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:58.696051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:30.652881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:02.668461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:34.853167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:06.673076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:38.650584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:10.638026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:41.543381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:13.197733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:44.484464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:16.259493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:47.358357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:04.100723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:15.186119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:26.274466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:37.425905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:48.529246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:45.066750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:59.179723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:31.140580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:03.129199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:35.347844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:07.148804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:39.110335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:11.128714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:42.005114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:13.676453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:44.929302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:16.729274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:47.806165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:04.575453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:15.640934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:26.675421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:37.869759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:48.986024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:48.045778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:17:59.667450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:31.636252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:03.609914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:35.833548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:07.650463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:39.612961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:11.604445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:42.500823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:14.135226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:45.411985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:17.206991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:48.302805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:05.016277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:16.113639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:27.115246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:38.299600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:49.509624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:26:51.052740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:00.160101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:32.106995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:04.106586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:36.331220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:08.121204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:40.076746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:12.061221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:42.967572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:14.642869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:45.861812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:17.687674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:48.768594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:05.506961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:16.614331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:27.614916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:38.743413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:49.912549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:27:04.482831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:08.716252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:18:40.692006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:12.725569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:19:44.706785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:16.552625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:20:48.454348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:20.478711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:21:51.330213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:23.062354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:22:54.088808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:26.012450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:23:57.033459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:08.379282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:19.476678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:30.498200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:41.629695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-20T13:24:52.787860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-20T13:29:46.696825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ARR_DELAYARR_TIMECANCELLATION_CODECANCELLEDCARRIER_DELAYDEP_DELAYDEP_TIMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDLATE_AIRCRAFT_DELAYMMYYYYNAS_DELAYOP_CARRIEROP_UNIQUE_CARRIERORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDSECURITY_DELAYTAXI_INTAXI_OUTWEATHER_DELAY
ARR_DELAY1.0000.1140.0001.0000.1910.6500.1620.0170.0170.0220.356-0.0340.0100.0180.018-0.007-0.008-0.029-0.0080.1100.2640.128
ARR_TIME0.1141.0000.0001.000-0.0650.1440.7580.0200.0200.0470.140-0.007-0.0150.0460.046-0.004-0.004-0.044-0.002-0.0260.0250.002
CANCELLATION_CODE0.0000.0001.0001.0000.0000.0480.1650.0650.0650.0920.0000.6000.0000.2870.2870.0710.0710.0950.0000.0000.0660.000
CANCELLED1.0001.0001.0001.0001.0000.0240.0100.0140.0140.0181.0000.2641.0000.0530.0530.0120.0120.0181.0001.0000.0041.000
CARRIER_DELAY0.191-0.0650.0001.0001.0000.299-0.0150.0040.0050.032-0.2240.057-0.3740.0200.020-0.046-0.046-0.056-0.056-0.118-0.141-0.220
DEP_DELAY0.6500.1440.0480.0240.2991.0000.2050.0110.0100.0240.468-0.025-0.3800.0170.017-0.032-0.033-0.0660.001-0.0480.0230.108
DEP_TIME0.1620.7580.1650.010-0.0150.2051.0000.0300.0300.0640.290-0.003-0.1330.0450.045-0.035-0.035-0.054-0.004-0.0670.0030.013
DEST_AIRPORT_ID0.0170.0200.0650.0140.0040.0110.0301.0001.0000.619-0.011-0.007-0.0030.1780.1780.0130.013-0.011-0.003-0.1270.023-0.012
DEST_AIRPORT_SEQ_ID0.0170.0200.0650.0140.0050.0100.0301.0001.0000.619-0.0110.001-0.0030.1780.1780.0130.013-0.011-0.003-0.1270.023-0.012
DEST_CITY_MARKET_ID0.0220.0470.0920.0180.0320.0240.0640.6190.6191.000-0.0130.003-0.0150.1580.158-0.011-0.011-0.059-0.001-0.2350.055-0.007
LATE_AIRCRAFT_DELAY0.3560.1400.0001.000-0.2240.4680.290-0.011-0.011-0.0131.000-0.015-0.3290.0160.0160.0270.0270.032-0.016-0.094-0.217-0.036
MMYYYY-0.034-0.0070.6000.2640.057-0.025-0.003-0.0070.0010.003-0.0151.000-0.0570.1080.108-0.0070.0010.0030.0190.0230.0460.007
NAS_DELAY0.010-0.0150.0001.000-0.374-0.380-0.133-0.003-0.003-0.015-0.329-0.0571.0000.0200.0200.0080.0070.024-0.0150.2850.468-0.010
OP_CARRIER0.0180.0460.2870.0530.0200.0170.0450.1780.1780.1580.0160.1080.0201.0001.0000.1780.1780.1580.0030.0020.0500.013
OP_UNIQUE_CARRIER0.0180.0460.2870.0530.0200.0170.0450.1780.1780.1580.0160.1080.0201.0001.0000.1780.1780.1580.0030.0020.0500.013
ORIGIN_AIRPORT_ID-0.007-0.0040.0710.012-0.046-0.032-0.0350.0130.013-0.0110.027-0.0070.0080.1780.1781.0001.0000.619-0.0070.044-0.034-0.026
ORIGIN_AIRPORT_SEQ_ID-0.008-0.0040.0710.012-0.046-0.033-0.0350.0130.013-0.0110.0270.0010.0070.1780.1781.0001.0000.619-0.0070.045-0.034-0.026
ORIGIN_CITY_MARKET_ID-0.029-0.0440.0950.018-0.056-0.066-0.054-0.011-0.011-0.0590.0320.0030.0240.1580.1580.6190.6191.0000.0020.112-0.061-0.032
SECURITY_DELAY-0.008-0.0020.0001.000-0.0560.001-0.004-0.003-0.003-0.001-0.0160.019-0.0150.0030.003-0.007-0.0070.0021.000-0.004-0.011-0.015
TAXI_IN0.110-0.0260.0001.000-0.118-0.048-0.067-0.127-0.127-0.235-0.0940.0230.2850.0020.0020.0440.0450.112-0.0041.0000.060-0.004
TAXI_OUT0.2640.0250.0660.004-0.1410.0230.0030.0230.0230.055-0.2170.0460.4680.0500.050-0.034-0.034-0.061-0.0110.0601.0000.071
WEATHER_DELAY0.1280.0020.0001.000-0.2200.1080.013-0.012-0.012-0.007-0.0360.007-0.0100.0130.013-0.026-0.026-0.032-0.015-0.0040.0711.000

Missing values

2024-09-20T13:27:09.681057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-20T13:27:31.198170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-20T13:29:07.425448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEOP_UNIQUE_CARRIEROP_CARRIERTAIL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAYMMYYYY
02014-07-16AAAAN3GAAA13303133030332467MIAMiami, FL14107141070230466PHXPhoenix, AZ1658.03.021.08.01844.04.00.0NaNNaNNaNNaNNaNNaN201407
12014-07-16VXVXN634VA12478124780231703JFKNew York, NY14771147710132457SFOSan Francisco, CA1151.081.023.04.01450.065.00.0NaN65.00.00.00.00.0201407
22014-07-05DLDLN974DL13204132040231454MCOOrlando, FL12953129530231703LGANew York, NY1019.0-5.010.07.01239.0-20.00.0NaNNaNNaNNaNNaNNaN201407
32014-07-05WNWNN924WN12892128920332575LAXLos Angeles, CA15016150160331123STLSt. Louis, MO1608.0-2.012.04.02136.0-14.00.0NaNNaNNaNNaNNaNNaN201407
42014-07-21EVEVN691CA10397103970530397ATLAtlanta, GA15919159190231834XNAFayetteville, AR1352.0-3.010.03.01423.0-24.00.0NaNNaNNaNNaNNaNNaN201407
52014-07-15WNWNN484WN14831148310332457SJCSan Jose, CA14107141070230466PHXPhoenix, AZ834.0-1.09.06.01020.0-5.00.0NaNNaNNaNNaNNaNNaN201407
62014-07-24OOOON929EV14771147710132457SFOSan Francisco, CA10800108000332575BURBurbank, CA1630.0-5.033.04.01800.06.00.0NaNNaNNaNNaNNaNNaN201407
72014-07-16WNWNN8308K12191121910231453HOUHouston, TX12892128920332575LAXLos Angeles, CA2053.043.07.09.02204.034.00.0NaN0.00.00.00.034.0201407
82014-07-17OOOON762SK13930139300330977ORDChicago, IL13851138510333851OKCOklahoma City, OK1852.059.016.06.02058.062.00.0NaN59.00.03.00.00.0201407
92014-07-16AAAAN634AA10721107210230721BOSBoston, MA13303133030332467MIAMiami, FL944.034.024.08.01313.033.00.0NaN0.00.033.00.00.0201407
FL_DATEOP_UNIQUE_CARRIEROP_CARRIERTAIL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGIN_CITY_MARKET_IDORIGINORIGIN_CITY_NAMEDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEST_CITY_MARKET_IDDESTDEST_CITY_NAMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAYMMYYYY
119913482019-09-24AAAAN983AN13303133030332467MIAMiami, FL12953129530431703LGANew York, NY731.0-5.012.08.01027.0-8.00.0NaNNaNNaNNaNNaNNaN201909
119913492019-09-09WNWNN8311Q13495134950533495MSYNew Orleans, LA13232132320230977MDWChicago, IL703.0-2.09.03.0908.0-12.00.0NaNNaNNaNNaNNaNNaN201909
119913502019-09-17YXYXN874RW12953129530431703LGANew York, NY11066110660631066CMHColumbus, OH2023.093.052.04.02231.0104.00.0NaN1.00.011.00.092.0201909
119913512019-09-15ASASN925VA14747147470330559SEASeattle, WA14771147710432457SFOSan Francisco, CA1732.02.019.03.02000.020.00.0NaN0.00.020.00.00.0201909
119913522019-09-06OOOON173SY10800108000332575BURBurbank, CA14831148310632457SJCSan Jose, CA2019.0-6.09.04.02128.0-12.00.0NaNNaNNaNNaNNaNNaN201909
119913532019-09-26OHOHN557NN14100141000534100PHLPhiladelphia, PA11193111930233105CVGCincinnati, OH2037.0-3.015.02.02210.0-17.00.0NaNNaNNaNNaNNaNNaN201909
119913542019-09-29YVYVN519LR12953129530431703LGANew York, NY12264122640230852IADWashington, DC1018.0-2.029.010.01145.0-1.00.0NaNNaNNaNNaNNaNNaN201909
119913552019-09-10WNWNN8547V11292112920230325DENDenver, CO11618116180231703EWRNewark, NJ1720.00.016.07.02245.0-15.00.0NaNNaNNaNNaNNaNNaN201909
119913562019-09-23WNWNN7735A11042110420530647CLECleveland, OH10397103970730397ATLAtlanta, GA1654.0-1.08.04.01827.0-23.00.0NaNNaNNaNNaNNaNNaN201909
119913572019-09-09WNWNN498WN10529105290630529BDLHartford, CT10821108210630852BWIBaltimore, MD1748.0-7.010.013.01900.0-15.00.0NaNNaNNaNNaNNaNNaN201909